Maia Analysis
Maia Czerwonka
2024-02-27
# Load in .csv
data <- read.csv("MaiaData_CardSort.csv")
data## Subject Age Sex IncorRespCount SymRespCount TxtRespCount BiasScore
## 1 0156ce12 51 2 0 9 39 0.62500000
## 2 054ba968 87 2 0 20 28 0.16666667
## 3 05546b7f 60 2 5 12 31 0.44186046
## 4 08f746fa 38 2 2 39 7 -0.69565217
## 5 0aef8687 63 2 8 8 32 0.60000000
## 6 0b2a2504 34 2 4 17 27 0.22727273
## 7 0b482cbe 36 2 1 44 3 -0.87234043
## 8 0bf6b839 54 2 0 45 3 -0.87500000
## 9 0ccd33c5 54 2 0 14 34 0.41666667
## 10 0cf98284 45 2 1 11 36 0.53191489
## 11 0ebb6dab 80 2 0 1 47 0.95833333
## 12 107f8d3e 29 2 0 45 3 -0.87500000
## 13 117d6806 22 2 3 41 4 -0.82222222
## 14 14165170 83 3 3 1 44 0.95555556
## 15 16f9e2af 51 2 0 1 47 0.95833333
## 16 17d323d9 46 2 15 17 16 -0.03030303
## 17 188a146f 34 2 3 3 42 0.86666667
## 18 1aa15bd9 25 2 1 0 47 1.00000000
## 19 1c4f7ffb 44 1 2 38 8 -0.65217391
## 20 1d937134 34 2 1 45 2 -0.91489362
## 21 1ea6c7bb 32 1 0 4 44 0.83333333
## 22 2078bec9 10 2 2 42 4 -0.82608696
## 23 214a186c 54 2 1 25 22 -0.06382979
## 24 2485948d 51 2 1 44 3 -0.87234043
## 25 24d99a95 39 2 1 40 7 -0.70212766
## 26 2589c6ec 36 2 2 1 45 0.95652174
## 27 2887f859 36 2 1 35 12 -0.48936170
## 28 28a198fb 15 2 0 0 48 1.00000000
## 29 2958c25f 36 2 0 18 30 0.25000000
## 30 297c34a5 44 2 2 41 5 -0.78260870
## 31 2a91a3cc 60 1 1 46 1 -0.95744681
## 32 2c32d3f4 62 1 1 17 30 0.27659575
## 33 2dc7132a 38 2 0 12 36 0.50000000
## 34 2e5e60b7 33 2 1 2 45 0.91489362
## 35 2ee97ea7 29 1 0 45 3 -0.87500000
## 36 30d40fe2 26 1 3 44 1 -0.95555556
## 37 32308591 35 2 0 34 14 -0.41666667
## 38 32813fd0 60 1 1 3 44 0.87234043
## 39 335d619c 25 2 2 20 26 0.13043478
## 40 3456c8ae 35 4 1 1 46 0.95744681
## 41 36345909 23 2 0 1 47 0.95833333
## 42 3661b157 40 1 1 2 45 0.91489362
## 43 3b118efc 42 2 1 0 47 1.00000000
## 44 3b79c957 37 2 2 38 8 -0.65217391
## 45 3e459806 40 2 0 0 48 1.00000000
## 46 3e83a772 30 2 3 44 1 -0.95555556
## 47 400eed91 21 2 0 3 45 0.87500000
## 48 42bd8614 50 1 1 4 43 0.82978723
## 49 431d20df 45 2 0 43 5 -0.79166667
## 50 450af0e8 27 2 0 37 11 -0.54166667
## 51 46db6023 22 1 3 41 4 -0.82222222
## 52 4845278d 41 1 16 17 15 -0.06250000
## 53 4db4c716 30 2 1 8 39 0.65957447
## 54 4ea7e33e 45 2 1 34 13 -0.44680851
## 55 513c6adf 27 1 1 2 45 0.91489362
## 56 53124fc7 31 2 0 6 42 0.75000000
## 57 53afc254 20 1 1 40 7 -0.70212766
## 58 549e2bf3 23 2 1 2 45 0.91489362
## 59 54d4291b 41 2 1 44 3 -0.87234043
## 60 5701e616 64 2 14 12 22 0.29411765
## 61 5816ece9 19 2 3 36 9 -0.60000000
## 62 5843f329 54 1 2 1 45 0.95652174
## 63 59348bca 47 2 2 3 43 0.86956522
## 64 599a551a 32 2 0 7 41 0.70833333
## 65 59d4e173 53 2 0 1 47 0.95833333
## 66 5c8d0b14 45 2 9 26 13 -0.33333333
## 67 5ee0a865 15 2 2 40 6 -0.73913043
## 68 5fc4fa1b 12 2 1 17 30 0.27659575
## 69 61f78fb4 42 2 3 1 44 0.95555556
## 70 62c71be3 25 2 2 9 37 0.60869565
## 71 6409a8b2 22 2 10 24 14 -0.26315789
## 72 65c75fc2 34 2 1 2 45 0.91489362
## 73 66cfde31 56 2 1 1 46 0.95744681
## 74 66eb7b6a 20 2 1 9 38 0.61702128
## 75 66fab246 22 2 1 45 2 -0.91489362
## 76 68a9d481 44 2 3 2 43 0.91111111
## 77 69863308 40 2 2 15 31 0.34782609
## 78 703265e2 43 1 3 0 45 1.00000000
## 79 714b2d11 37 2 0 1 47 0.95833333
## 80 73f3b4e7 39 2 1 3 44 0.87234043
## 81 7c320256 24 2 1 3 44 0.87234043
## 82 7c99f480 32 2 2 40 6 -0.73913043
## 83 7f2bf12c NA 4 0 7 41 0.70833333
## 84 80884a14 33 2 3 21 24 0.06666667
## 85 82a2bd0c 42 2 2 4 42 0.82608696
## 86 86655f6c 29 2 3 4 41 0.82222222
## 87 88a049b1 46 2 1 1 46 0.95744681
## 88 88f5e293 49 2 2 1 45 0.95652174
## 89 89601069 22 2 0 23 25 0.04166667
## 90 8afceed7 47 2 1 1 46 0.95744681
## 91 8f5c3e5c 55 2 0 43 5 -0.79166667
## 92 901e335b 44 2 1 0 47 1.00000000
## 93 90caab28 32 1 1 0 47 1.00000000
## 94 90db8365 58 2 0 0 48 1.00000000
## 95 90fd8c3c 52 2 13 14 21 0.20000000
## 96 91468fb8 22 2 2 44 2 -0.91304348
## 97 9267045e 36 2 3 16 29 0.28888889
## 98 93eb1c7c 51 1 1 45 2 -0.91489362
## 99 93f8eaf0 NA NA 2 1 45 0.95652174
## 100 9449a552 57 2 19 17 12 -0.17241379
## 101 96341682 31 1 1 0 47 1.00000000
## 102 98424747 32 2 1 6 41 0.74468085
## 103 99056ad5 66 2 1 20 27 0.14893617
## 104 9c2294e3 50 1 1 25 22 -0.06382979
## 105 9cabc628 62 2 0 9 39 0.62500000
## 106 9cae589f 37 1 2 42 4 -0.82608696
## 107 9d172f36 37 2 0 30 18 -0.25000000
## 108 9e16af1d 50 2 0 2 46 0.91666667
## 109 a20c2c7f 50 1 0 3 45 0.87500000
## 110 a316c167 41 1 1 40 7 -0.70212766
## 111 a4e92a6d 52 2 0 0 48 1.00000000
## 112 a76f91d1 33 2 1 40 7 -0.70212766
## 113 a8f7199a 34 2 1 39 8 -0.65957447
## 114 a9d90d7b 31 1 0 47 1 -0.95833333
## 115 ad20acda 43 1 0 2 46 0.91666667
## 116 ae6e621d 33 2 0 1 47 0.95833333
## 117 b0798a36 22 2 0 44 4 -0.83333333
## 118 b1466d2f 78 2 1 0 47 1.00000000
## 119 b34c5e2c 64 1 1 1 46 0.95744681
## 120 b54d7127 54 2 0 0 48 1.00000000
## 121 b5a17561 56 2 2 13 33 0.43478261
## 122 b68374cc 22 2 2 1 45 0.95652174
## 123 b6859fc4 23 2 3 4 41 0.82222222
## 124 b8f82017 39 2 0 47 1 -0.95833333
## 125 ba441428 34 2 0 2 46 0.91666667
## 126 bad0b9f0 34 2 0 30 18 -0.25000000
## 127 bad632c6 38 1 4 15 29 0.31818182
## 128 bb626e6d 28 2 1 27 20 -0.14893617
## 129 bc551487 28 1 2 2 44 0.91304348
## 130 bd06a398 44 2 2 0 46 1.00000000
## 131 bd5495a7 36 2 2 0 46 1.00000000
## 132 be60cc2c 30 2 0 1 47 0.95833333
## 133 beb01630 22 2 2 38 8 -0.65217391
## 134 c3224966 45 2 1 5 42 0.78723404
## 135 c5f1529a 45 2 2 38 8 -0.65217391
## 136 c6dec119 21 2 2 31 15 -0.34782609
## 137 c97ba508 35 1 0 0 48 1.00000000
## 138 cac5cbcc 38 2 1 1 46 0.95744681
## 139 cb8d96e2 63 2 0 2 46 0.91666667
## 140 ccabde00 38 2 1 3 44 0.87234043
## 141 cd55f156 30 1 0 45 3 -0.87500000
## 142 d0d04e2f 30 2 0 0 48 1.00000000
## 143 d208937c 74 2 0 1 47 0.95833333
## 144 d41bb601 43 2 1 1 46 0.95744681
## 145 d541e4c0 22 1 3 38 7 -0.68888889
## 146 d6dead27 22 2 0 45 3 -0.87500000
## 147 d792be63 65 2 0 0 48 1.00000000
## 148 d7f2ce5b 13 2 3 39 6 -0.73333333
## 149 d84d792b 58 2 2 2 44 0.91304348
## 150 d85d3220 25 2 0 46 2 -0.91666667
## 151 d9a70fac 63 2 3 2 43 0.91111111
## 152 da94862b 41 2 0 45 3 -0.87500000
## 153 dc8267e2 56 2 2 33 13 -0.43478261
## 154 dcee3b58 48 2 1 11 36 0.53191489
## 155 df52db19 21 2 14 21 13 -0.23529412
## 156 dfe0dfb3 40 1 2 43 3 -0.86956522
## 157 e0216ded 54 2 0 0 48 1.00000000
## 158 e1264a4f 26 2 0 2 46 0.91666667
## 159 e15dad59 46 1 2 19 27 0.17391304
## 160 e7ff23cc 39 2 13 15 20 0.14285714
## 161 e80f9781 44 2 0 15 33 0.37500000
## 162 e8b26ab1 47 1 0 13 35 0.45833333
## 163 e90ecf36 52 2 1 0 47 1.00000000
## 164 e928f889 35 1 0 47 1 -0.95833333
## 165 e9957f47 39 2 1 1 46 0.95744681
## 166 ea0fcf6f 51 2 0 46 2 -0.91666667
## 167 ea946a6b 30 2 0 2 46 0.91666667
## 168 ec01fb3e 35 1 3 45 0 -1.00000000
## 169 ec41c586 21 2 2 3 43 0.86956522
## 170 eca1bcfe 44 2 1 4 43 0.82978723
## 171 ecca85f8 38 1 2 43 3 -0.86956522
## 172 ed2aa13b 26 1 0 1 47 0.95833333
## 173 f0d24353 47 1 1 0 47 1.00000000
## 174 f1fb3ed7 48 1 1 21 26 0.10638298
## 175 f27c8cbe 58 1 0 48 0 -1.00000000
## 176 f33cd5ba 65 2 1 1 46 0.95744681
## 177 f6292664 31 2 0 42 6 -0.75000000
## 178 f85a3e1b 34 2 1 31 16 -0.31914894
## 179 f9af1669 42 2 0 45 3 -0.87500000
## 180 fa217a97 70 1 0 2 46 0.91666667
## 181 fade4881 59 2 13 16 19 0.08571429
## 182 fb705df2 18 2 3 37 8 -0.64444444
## 183 fdd4712a 41 2 3 32 13 -0.42222222
## 184 feabc45c 33 2 0 44 4 -0.83333333
## 185 ffdf4f28 30 2 1 0 47 1.00000000
## Con_RT InCon_RT Incon.ConRT
## 1 878.3542 1014.1875 135.8333333
## 2 1192.7917 1273.8125 81.0208333
## 3 1122.9306 1194.4583 71.5277778
## 4 1013.8264 1029.7708 15.9444444
## 5 990.1875 988.1875 -2.0000000
## 6 1066.3611 1098.3750 32.0138889
## 7 829.4028 954.7917 125.3888889
## 8 647.6250 728.7500 81.1250000
## 9 1119.5208 1274.3750 154.8541667
## 10 847.7083 913.3542 65.6458333
## 11 860.7292 871.4375 10.7083333
## 12 621.0694 710.6042 89.5347222
## 13 579.7847 605.9583 26.1736111
## 14 1056.6111 1070.5000 13.8888889
## 15 702.0417 698.2292 -3.8125000
## 16 893.2222 872.2083 -21.0138889
## 17 744.6597 791.7083 47.0486111
## 18 705.4097 646.8542 -58.5555556
## 19 948.7778 1282.8750 334.0972222
## 20 1126.0000 1313.3750 187.3750000
## 21 732.3611 690.7708 -41.5902778
## 22 837.9792 849.5625 11.5833333
## 23 895.5972 927.4375 31.8402778
## 24 1088.8056 1229.0208 140.2152778
## 25 1040.5486 1117.3750 76.8263889
## 26 542.7569 537.4167 -5.3402778
## 27 1136.2639 1393.7083 257.4444444
## 28 738.5000 735.3333 -3.1666667
## 29 883.0625 1100.8333 217.7708333
## 30 977.7153 1139.2708 161.5555556
## 31 1106.5278 1143.9167 37.3888889
## 32 1091.7708 1185.0833 93.3125000
## 33 1068.6875 1257.8125 189.1250000
## 34 807.9236 817.2708 9.3472222
## 35 912.3333 1066.9167 154.5833333
## 36 829.2292 894.3125 65.0833333
## 37 794.5903 846.1875 51.5972222
## 38 904.7778 838.1667 -66.6111111
## 39 722.9028 732.1875 9.2847222
## 40 673.9722 665.7083 -8.2638889
## 41 623.0764 612.8125 -10.2638889
## 42 862.1389 853.4583 -8.6805556
## 43 765.3056 752.3958 -12.9097222
## 44 820.5556 884.9583 64.4027778
## 45 778.8819 800.9167 22.0347222
## 46 603.7778 629.3958 25.6180556
## 47 529.0764 528.2917 -0.7847222
## 48 1006.2431 944.1458 -62.0972222
## 49 741.0694 923.5625 182.4930556
## 50 745.6944 859.7500 114.0555556
## 51 742.4306 856.2083 113.7777778
## 52 937.1181 1015.7708 78.6527778
## 53 906.2292 939.9167 33.6875000
## 54 775.1319 920.0000 144.8680556
## 55 764.0694 831.8333 67.7638889
## 56 1019.2708 1119.8542 100.5833333
## 57 899.9583 897.1875 -2.7708333
## 58 658.1667 616.8542 -41.3125000
## 59 1036.2083 1138.9583 102.7500000
## 60 1309.0208 1297.0000 -12.0208333
## 61 691.3542 738.4792 47.1250000
## 62 823.1806 770.8750 -52.3055556
## 63 984.2778 1029.2500 44.9722222
## 64 972.3750 982.6042 10.2291667
## 65 884.4375 879.6458 -4.7916667
## 66 1089.6806 1430.5208 340.8402778
## 67 890.7153 996.9167 106.2013889
## 68 1023.7569 1119.7500 95.9930556
## 69 813.7917 799.5000 -14.2916667
## 70 1215.9306 1260.7917 44.8611111
## 71 1176.2986 1060.2083 -116.0902778
## 72 806.4722 825.4167 18.9444444
## 73 680.7014 737.3958 56.6944444
## 74 829.9583 952.0625 122.1041667
## 75 857.7014 848.7292 -8.9722222
## 76 672.4583 697.6042 25.1458333
## 77 1209.5208 1219.1667 9.6458333
## 78 893.2292 895.1458 1.9166667
## 79 995.5694 1071.9583 76.3888889
## 80 933.4167 982.5625 49.1458333
## 81 941.6528 1174.0833 232.4305556
## 82 738.0764 763.2917 25.2152778
## 83 771.2917 796.7708 25.4791667
## 84 740.1111 748.7917 8.6805556
## 85 991.9097 1180.7917 188.8819444
## 86 1004.3750 953.7083 -50.6666667
## 87 1043.6389 1130.7292 87.0902778
## 88 707.2153 686.2500 -20.9652778
## 89 730.3403 673.3542 -56.9861111
## 90 1133.5833 1066.0000 -67.5833333
## 91 817.0417 986.9792 169.9375000
## 92 580.1181 628.8750 48.7569444
## 93 815.8333 823.1458 7.3125000
## 94 963.2569 979.1875 15.9305556
## 95 1213.4722 2693.2708 1479.7986110
## 96 852.2708 1059.8125 207.5416667
## 97 942.7569 1351.2292 408.4722222
## 98 874.8264 968.2708 93.4444444
## 99 678.2708 795.8958 117.6250000
## 100 1579.7153 1494.5625 -85.1527778
## 101 899.0972 945.6667 46.5694444
## 102 714.3611 895.3750 181.0138889
## 103 1260.3750 1705.9792 445.6041667
## 104 1225.6597 1620.2292 394.5694444
## 105 1198.6806 1229.4792 30.7986111
## 106 758.1528 822.0000 63.8472222
## 107 1232.2014 1605.5208 373.3194444
## 108 942.5625 925.8125 -16.7500000
## 109 826.6319 800.1042 -26.5277778
## 110 901.7431 1024.5000 122.7569444
## 111 849.4931 807.2917 -42.2013889
## 112 1175.7917 1815.8542 640.0625000
## 113 998.9583 1133.0208 134.0625000
## 114 910.7778 1085.3125 174.5347222
## 115 934.1736 970.9583 36.7847222
## 116 780.2639 862.5833 82.3194444
## 117 886.9097 876.7917 -10.1180556
## 118 1204.0625 1090.8958 -113.1666667
## 119 895.0069 916.9167 21.9097222
## 120 823.1250 797.0625 -26.0625000
## 121 827.9028 975.0208 147.1180556
## 122 749.9861 767.9375 17.9513889
## 123 631.3472 673.6875 42.3402778
## 124 810.7639 871.3542 60.5902778
## 125 702.7083 764.2083 61.5000000
## 126 867.2222 1008.1458 140.9236111
## 127 1044.6875 1127.5833 82.8958333
## 128 829.2083 1059.2292 230.0208333
## 129 785.0486 846.5417 61.4930556
## 130 945.1875 1015.7083 70.5208333
## 131 671.3542 672.1667 0.8125000
## 132 637.9167 672.8542 34.9375000
## 133 648.0833 654.6042 6.5208333
## 134 794.2014 995.8125 201.6111111
## 135 943.7639 1133.8125 190.0486111
## 136 706.6389 839.5625 132.9236111
## 137 738.3819 767.6042 29.2222222
## 138 850.4167 854.6875 4.2708333
## 139 787.3750 802.5208 15.1458333
## 140 768.5139 914.3125 145.7986111
## 141 800.1458 803.9792 3.8333333
## 142 939.9792 1051.3125 111.3333333
## 143 1121.3472 1124.1667 2.8194444
## 144 875.6597 900.8125 25.1527778
## 145 832.9722 817.9375 -15.0347222
## 146 862.1389 895.6250 33.4861111
## 147 853.6806 868.6458 14.9652778
## 148 606.1667 624.2708 18.1041667
## 149 734.6458 737.2917 2.6458333
## 150 648.6944 687.0208 38.3263889
## 151 952.1458 978.7708 26.6250000
## 152 927.7569 1090.1042 162.3472222
## 153 920.1111 1251.1875 331.0763889
## 154 914.5833 1111.3750 196.7916667
## 155 866.4167 881.4792 15.0625000
## 156 1307.9375 1449.2083 141.2708333
## 157 615.0417 610.6042 -4.4375000
## 158 1235.2431 1160.2083 -75.0347222
## 159 859.8681 948.0625 88.1944444
## 160 999.5833 2790.1667 1790.5833330
## 161 815.6875 911.2917 95.6041667
## 162 702.1944 716.8542 14.6597222
## 163 864.0278 794.0625 -69.9652778
## 164 921.6806 931.1458 9.4652778
## 165 964.9653 850.6042 -114.3611111
## 166 853.8472 1119.3958 265.5486111
## 167 867.6944 933.9167 66.2222222
## 168 792.6528 884.8333 92.1805556
## 169 716.9306 768.6250 51.6944444
## 170 963.7153 1019.8958 56.1805556
## 171 784.5417 1048.6042 264.0625000
## 172 683.2986 658.4167 -24.8819444
## 173 619.9306 626.3958 6.4652778
## 174 1163.1181 1463.3750 300.2569444
## 175 888.2361 928.7708 40.5347222
## 176 902.5208 893.0833 -9.4375000
## 177 845.6250 1391.8750 546.2500000
## 178 939.3819 1038.5000 99.1180556
## 179 810.0833 888.2083 78.1250000
## 180 815.9514 822.8542 6.9027778
## 181 960.7986 2863.0833 1902.2847220
## 182 1192.3681 1345.3125 152.9444444
## 183 988.2431 1345.1042 356.8611111
## 184 935.5833 1082.7708 147.1875000
## 185 1024.2500 1055.7500 31.50000001 Descriptives of Dataset
data %>%
select(-c(Subject, Sex)) %>%
psych::describe()## vars n mean sd median trimmed mad min max
## Age 1 183 40.05 14.52 38.00 39.22 13.34 10.00 87.00
## IncorRespCount 2 185 1.83 3.12 1.00 1.13 1.48 0.00 19.00
## SymRespCount 3 185 18.10 17.66 12.00 16.96 16.31 0.00 48.00
## TxtRespCount 4 185 28.07 17.95 33.00 28.91 20.76 0.00 48.00
## BiasScore 5 185 0.21 0.76 0.44 0.25 0.77 -1.00 1.00
## Con_RT 6 185 885.79 174.50 866.42 875.88 160.51 529.08 1579.72
## InCon_RT 7 185 987.53 325.00 925.81 946.21 209.20 528.29 2863.08
## Incon.ConRT 8 185 101.74 239.42 46.57 62.58 74.70 -116.09 1902.28
## range skew kurtosis se
## Age 77.00 0.55 0.20 1.07
## IncorRespCount 19.00 3.35 11.70 0.23
## SymRespCount 48.00 0.45 -1.49 1.30
## TxtRespCount 48.00 -0.31 -1.61 1.32
## BiasScore 2.00 -0.38 -1.55 0.06
## Con_RT 1050.64 0.63 0.59 12.83
## InCon_RT 2334.79 2.91 13.12 23.89
## Incon.ConRT 2018.37 5.34 33.94 17.602 Distributions of Bias Scores
biasMean <- mean(data$BiasScore, na.rm = T)
biasMedian <- median(data$BiasScore, na.rm = T)
bias1sd <- biasMean + sd(data$BiasScore)
biasneg1sd <- biasMean - sd(data$BiasScore)
data %>%
ggplot(aes(x=BiasScore)) +
geom_histogram(fill = "grey", color = "black") +
geom_vline(mapping = aes(xintercept = biasMean), color = "red", linetype = "dashed", size = 1) +
geom_vline(mapping = aes(xintercept = biasMedian), color = "blue", linetype = "dashed", size = 1) +
geom_vline(mapping = aes(xintercept = bias1sd), color = "black", linetype = "dashed", size = 1) +
geom_vline(mapping = aes(xintercept = biasneg1sd), color = "black", linetype = "dashed", size = 1) +
ggtitle("Distribution of Bias Scores")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data %>%
ggplot(aes(x=BiasScore)) +
geom_density(fill = "grey", color = "black") +
geom_vline(mapping = aes(xintercept = biasMean), color = "red", linetype = "dashed", size = 1) +
geom_vline(mapping = aes(xintercept = biasMedian), color = "blue", linetype = "dashed", size = 1) +
ggtitle("Distribution of Bias Scores")
#A lot less neutral people than biased people
3 Distributions of Incongruency Effect - Whole Group & Bias Split
# Whole group distribution of incongruency effect
data %>%
ggplot(aes(x=Incon.ConRT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Incongruency Rt Effects")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data %>%
ggplot(aes(x=InCon_RT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Incongruency RT")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data %>%
ggplot(aes(x=Con_RT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Congruency RT")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Split groups based on bias score
biased <- subset(data, data$BiasScore > 0.8 | data$BiasScore < -0.8)
neutral <- subset(data, data$BiasScore <= 0.8 & data$BiasScore >= -0.8)
biased$Group = "biased"
neutral$Group = "neutral"
data_grouped <- rbind(biased, neutral)
data_grouped %>%
ggplot(aes(x = BiasScore, fill = Group))+ geom_vline(mapping = aes(xintercept = biasMean), color = "red", linetype = "dashed", size = 1) +
geom_vline(mapping = aes(xintercept = biasMedian), color = "blue", linetype = "dashed", size = 1) +
geom_histogram(color="black") + ggtitle("Bias Group Split Distribution")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data_grouped %>%
ggplot(aes(x = Incon.ConRT, fill = Group, color = Group))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Incongruency RT Effects By Bias Group Split")
data_grouped %>%
ggplot(aes(x = InCon_RT, fill = Group, color = Group))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Incongruent RT By Bias Group Split")
neutral_incon_mean = mean(neutral$InCon_RT)
neutral_con_mean = mean(neutral$Con_RT)
biased_incon_mean = mean(biased$InCon_RT)
biased_con_mean = mean(biased$Con_RT)
data_grouped %>%
ggplot(aes(x = InCon_RT, fill = Group, color = Group))+
geom_density(alpha = 0.3)+geom_vline(mapping = aes(xintercept = neutral_incon_mean), color = "turquoise", linetype = "dashed", size = 1)+ geom_vline(mapping = aes(xintercept = biased_incon_mean), color = "magenta", linetype = "dashed", size = 1)+
ggtitle("Distribution of Incongruent RT By Bias Group Split")
data_grouped %>%
ggplot(aes(x = Con_RT, fill = Group, color = Group))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Congruent RT By Bias Group Split")
data_grouped %>%
ggplot(aes(x = Con_RT, fill = Group, color = Group))+
geom_density(alpha = 0.3)+geom_vline(mapping = aes(xintercept = neutral_con_mean), color = "turquoise", linetype = "dashed", size = 1)+ geom_vline(mapping = aes(xintercept = biased_con_mean), color = "magenta", linetype = "dashed", size = 1)+
ggtitle("Distribution of Congruent RT By Bias Group Split")
#Incongruency RT Effect Dist: Most people have an incongruency effect of
0 or > so, as expected, incongruent trial reaction times are
generally greater than congruent trial reaction times. Slight right skew
that is made extreme by outliers- will reexamine with outliers removed.
#Distribution of Incongruency Effect by Bias Group Split: There are more
people who have a lower incongruency effect in the biased attender group
than in the neutral attender group. Neutral attender group has a lot
less people but its density is a lot more spread out while biased
attender incongruency effect has a lot less range and is more
concentrated near 0. #Congruent and Incongruent Distributions by Bias
Group Split: Biased group seems to be taking less time than the neutral
group in both trial types. Could be indication of biased attenders
mainly paying attention to their preferred information processing style
and ignoring the other stimulus so RTs are faster in both trial
types.
4 Reaction times by trial type and attention
# Load necessary libraries
library(tidyverse)
library(ggplot2)
library(reshape2)
data$Attention <- ifelse(data$BiasScore > 0.8 | data$BiasScore < -0.8, "Biased", "Neutral")
data$IPS <- ifelse(data$BiasScore > 0, "Verbal", "Visual")
#Reaction times
#Biased attender histograms and descriptive statistics
biased_data<- data[data$Attention == "Biased", ]
neutral_data<- data[data$Attention == "Neutral", ]
# Combine biased and neutral data
data$Attention <- ifelse(data$BiasScore > 0.8 | data$BiasScore < -0.8, "Biased", "Neutral")
biased_data<- data[data$Attention == "Biased", ]
combined_data <- rbind(biased_data, neutral_data)
# Calculate means by attention and trial type
means <- combined_data %>%
group_by(Attention) %>%
summarise(Con_RT = mean(Con_RT), InCon_RT = mean(InCon_RT)) %>%
pivot_longer(cols = c(Con_RT, InCon_RT), names_to = "Trial_Type", values_to = "RT")
# Order factor levels for better plotting
means$Attention <- factor(means$Attention, levels = unique(means$Attention))
combined_long <- combined_data %>%
select(Attention, InCon_RT, Con_RT) %>%
pivot_longer(cols = c(InCon_RT, Con_RT), values_to = "RT", names_to = "Condition")
# Creat error bars
se_sum <- combined_long %>%
group_by(Attention, Condition) %>%
summarise(
sd = sd(RT),
n = n(),
mean = mean(RT)
) %>%
mutate(se = sd/sqrt(n))## `summarise()` has grouped output by 'Attention'. You can override using the
## `.groups` argument.# Create bar plot
ggplot(se_sum, aes(x = Attention, y = mean, fill = Condition)) +
geom_bar(position = position_dodge(0.8), stat = "identity", color = "black", size = 0.5, width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(0.8), width = 0.25, color = "black") +
labs(title = "Mean Reaction Times by Attention and Trial Type",
x = "Attention",
y = "Mean Reaction Time (ms)") +
theme_minimal()
combined_long_wid <- combined_data %>%
select(Attention, InCon_RT, Con_RT, Subject) %>%
pivot_longer(cols = c(InCon_RT, Con_RT), values_to = "RT", names_to = "Condition")#incon rts are larger overall, but the difference between incon and con trial rts for the biased group is a lot smaller than the neutral group- perhaps because the neutral group is noticing both stimuli more? #figure out how to put error bars
5 Incongruency Effect Analysis
5.1 Incongruency Effect Descriptives
# Incongruency Effect Calculation
data$IncongruencyEffect <- data$InCon_RT - data$Con_RT
Incongruency_Effect_Data <- data$IncongruencyEffect
biased_data$Incongruency_Effect_Data <- biased_data$InCon_RT - biased_data$Con_RT
neutral_data$Incongruency_Effect_Data <- neutral_data$InCon_RT - neutral_data$Con_RT5.1.1 Biased group descriptives
psych::describe(biased_data$Incongruency_Effect_Data)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 109 38.32 72.9 25.15 32.84 52.51 -114.36 265.55 379.91 0.85 0.97
## se
## X1 6.985.1.2 Neutral group descriptives
psych::describe(neutral_data$Incongruency_Effect_Data)## vars n mean sd median trimmed mad min max range skew
## X1 1 76 192.68 344.6 99.85 124.84 121.86 -116.09 1902.28 2018.37 3.6
## kurtosis se
## X1 13.72 39.53# Biased Attender Incongruency Effect Descriptive Statistics
biased_IE_mean <- mean(biased_data$Incongruency_Effect_Data)
biased_IE_std <- sd(biased_data$Incongruency_Effect_Data)
biased_IE_min <- min(biased_data$Incongruency_Effect_Data)
biased_IE_max <- max(biased_data$Incongruency_Effect_Data)
# Data frame for Biased Attender Incongruency Effect Descriptive Statistics
biased_descriptive_IE <- data.frame(
Attention = "Biased",
Variable = "Incongruency Effect",
Mean = biased_IE_mean,
StdDev = biased_IE_std,
Min = biased_IE_min,
Max = biased_IE_max,
stringsAsFactors = FALSE
)
# Neutral Attender Incongruency Effect Descriptive Statistics
neutral_IE_mean <- mean(neutral_data$Incongruency_Effect_Data)
neutral_IE_std <- sd(neutral_data$Incongruency_Effect_Data)
neutral_IE_min <- min(neutral_data$Incongruency_Effect_Data)
neutral_IE_max <- max(neutral_data$Incongruency_Effect_Data)
# Data frame for Neutral Attender Incongruency Effect Descriptive Statistics
neutral_descriptive_IE <- data.frame(
Attention = "Neutral",
Variable = "Incongruency Effect",
Mean = neutral_IE_mean,
StdDev = neutral_IE_std,
Min = neutral_IE_min,
Max = neutral_IE_max,
stringsAsFactors = FALSE
)
descriptive_statistics_IE <- rbind(biased_descriptive_IE, neutral_descriptive_IE)
print(descriptive_statistics_IE)## Attention Variable Mean StdDev Min Max
## 1 Biased Incongruency Effect 38.32225 72.8954 -114.3611 265.5486
## 2 Neutral Incongruency Effect 192.68412 344.5965 -116.0903 1902.28475.2 Incongruency Effect Box Plot
# Load necessary libraries
library(ggplot2)
# Combine biased and neutral data for the box plot
combined_data <- rbind(biased_data, neutral_data)
# Create box plot
ggplot(combined_data, aes(x = Attention, y = Incongruency_Effect_Data, fill = Attention)) +
geom_boxplot() +
labs(
title = "Incongruency Effect Box Plot",
x = "Attention",
y = "Incongruency Effect (ms)"
) +
scale_fill_manual(values = c("red", "lightblue")) + # Color for biased and neutral data
theme_minimal()
## Incongruency Effect Scatter Plot
# Scatter Plot of Incongruency Effect
plot(neutral_data$Incongruency_Effect_Data,
col = "blue",
pch = 16,
main = "Scatter Plot of Incongruency Effect",
xlab = "Incongruency Effect (ms)",
ylab = "Condition")
# Adding Biased Attender Data Points
points(biased_data$Incongruency_Effect_Data,
col = "red",
pch = 16)
6 Individual Differences Analysis
6.1 Correlate Bias score in whole group with incongruency RT effect
cor.test(data$BiasScore, data$Incon.ConRT)##
## Pearson's product-moment correlation
##
## data: data$BiasScore and data$Incon.ConRT
## t = -2.6908, df = 183, p-value = 0.007789
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33006526 -0.05228936
## sample estimates:
## cor
## -0.1950863data %>%
ggplot(aes(x = BiasScore, y = Incon.ConRT)) +
geom_point(size = 3, alpha = 0.5, fill = "grey") +
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
data$absBiasScore <- abs(data$BiasScore)
cor.test(data$absBiasScore, data$Incon.ConRT)##
## Pearson's product-moment correlation
##
## data: data$absBiasScore and data$Incon.ConRT
## t = -5.6186, df = 183, p-value = 7.071e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5001629 -0.2533216
## sample estimates:
## cor
## -0.383572data %>%
ggplot(aes(x = absBiasScore, y = Incon.ConRT)) +
geom_point(size = 3, alpha = 0.5, fill = "grey") +
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
#doesn’t look like much of a correlation, reexamine with outliers
removed.
combined_long_wid <- combined_data %>%
select(Attention, InCon_RT, Con_RT, Subject) %>%
pivot_longer(cols = c(InCon_RT, Con_RT), values_to = "RT", names_to = "Condition")
#anova_test(data=combined_long_wid, dv=RT, wid=Subject, between=Attention, within = Condition)6.2 T-test code - with full data (keeping outliers)
# Difference in Incon-Con RT between Attention Groups
biased<- data[data$Attention == "Biased", ]
neutral<- data[data$Attention == "Neutral", ]
#First test normality assumption in Neutral Group
qqnorm(neutral$Incon.ConRT, pch = 1, frame = FALSE, main = "Neutral Group: Incon - Con RT")
qqline(neutral$Incon.ConRT, col = "steelblue", lwd = 2)
shapiro.test(neutral$Incon.ConRT) # Assumption of normality is violated; probably due to outliers##
## Shapiro-Wilk normality test
##
## data: neutral$Incon.ConRT
## W = 0.5497, p-value = 6.594e-14#Then test normality assumption in Biased Group
qqnorm(biased$Incon.ConRT, pch = 1, frame = FALSE, main = "Biased Group: Incon - Con RT")
qqline(biased$Incon.ConRT, col = "darkred", lwd = 2)
shapiro.test(biased$Incon.ConRT) # Assumption of normality is marginally violated##
## Shapiro-Wilk normality test
##
## data: biased$Incon.ConRT
## W = 0.94661, p-value = 0.0002631#Check that the variance does not differ between groups
# Perform Levene's AT_FormTest
print(leveneTest(Incon.ConRT ~ Attention, data = combined_data)) # Variances are marginally different; assumption holds## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 12.698 0.0004668 ***
## 183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Conduct t-test with equal variance assumption
print(t.test(neutral$Incon.ConRT, biased$Incon.ConRT, var.equal = F)) # T-Test is significant after correwction for unequal variances ##
## Welch Two Sample t-test
##
## data: neutral$Incon.ConRT and biased$Incon.ConRT
## t = 3.8456, df = 79.699, p-value = 0.0002408
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 74.47641 234.24733
## sample estimates:
## mean of x mean of y
## 192.68412 38.32225# test normality
qqnorm(neutral$Con_RT, pch = 1, frame = FALSE, main = "Neutral Group: Con RT")
qqline(neutral$Con_RT, col = "steelblue", lwd = 2)
shapiro.test(neutral$Con_RT) #assumption of normality violated##
## Shapiro-Wilk normality test
##
## data: neutral$Con_RT
## W = 0.96673, p-value = 0.04339qqnorm(biased$Con_RT, pch = 1, frame = FALSE, main = "Biased Group: Con RT")
qqline(biased$Con_RT, col = "darkred", lwd = 2)
shapiro.test(biased$Con_RT)##
## Shapiro-Wilk normality test
##
## data: biased$Con_RT
## W = 0.98426, p-value = 0.2281# check if variance differs between groups
print(leveneTest(Con_RT ~ Attention, data = combined_data)) #assumption of homogeneity violated## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 3.3796 0.06763 .
## 183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#do t-test
print(t.test(neutral$Con_RT, biased$Con_RT, var.equal = F))##
## Welch Two Sample t-test
##
## data: neutral$Con_RT and biased$Con_RT
## t = 4.4737, df = 141.06, p-value = 1.57e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 63.90959 165.11498
## sample estimates:
## mean of x mean of y
## 953.2613 838.7490# test normality
qqnorm(neutral$InCon_RT, pch = 1, frame = FALSE, main = "Neutral Group: InCon RT")
qqline(neutral$InCon_RT, col = "steelblue", lwd = 2)
shapiro.test(neutral$InCon_RT) ##
## Shapiro-Wilk normality test
##
## data: neutral$InCon_RT
## W = 0.75974, p-value = 8.108e-10qqnorm(biased$InCon_RT, pch = 1, frame = FALSE, main = "Biased Group: InCon RT")
qqline(biased$InCon_RT, col = "darkred", lwd = 2)
shapiro.test(biased$InCon_RT) #assumption of normality violated##
## Shapiro-Wilk normality test
##
## data: biased$InCon_RT
## W = 0.98125, p-value = 0.1282# check if variance differs between groups
print(leveneTest(InCon_RT ~ Attention, data = combined_data)) #assumption of homogeneity not violated## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 12.393 0.0005437 ***
## 183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#do t-test
print(t.test(neutral$InCon_RT, biased$InCon_RT, var.equal = T))##
## Two Sample t-test
##
## data: neutral$InCon_RT and biased$InCon_RT
## t = 6.0477, df = 183, p-value = 8.089e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 181.1555 356.5928
## sample estimates:
## mean of x mean of y
## 1145.9454 877.07137 Removing Outliers InCon RT
Incon_minus3SD <- mean(data$InCon_RT) - (3* sd(data$InCon_RT))
Incon_plus3SD <- mean(data$InCon_RT) + (3* sd(data$InCon_RT))
data <- data %>%
mutate(InconOutlier = InCon_RT >= Incon_plus3SD)
outliers_subset = subset(data, data$InconOutlier == TRUE)
data_outliersremoved <- subset(data, data$InconOutlier == FALSE)7.1 InCon Outliers Removed Descriptives
data_outliersremoved %>%
select(-c(Subject, Sex)) %>%
psych::describe()## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## vars n mean sd median trimmed mad min max
## Age 1 180 39.89 14.54 38.00 38.99 13.34 10.00 87.00
## IncorRespCount 2 182 1.64 2.79 1.00 1.10 1.48 0.00 19.00
## SymRespCount 3 182 18.15 17.80 12.00 17.00 16.31 0.00 48.00
## TxtRespCount 4 182 28.20 18.07 34.50 29.10 18.53 0.00 48.00
## BiasScore 5 182 0.21 0.76 0.48 0.26 0.76 -1.00 1.00
## Con_RT 6 182 882.95 173.93 863.08 872.69 158.80 529.08 1579.72
## InCon_RT 7 182 957.95 230.28 921.78 939.41 209.53 528.29 1815.85
## Incon.ConRT 8 182 74.99 115.71 44.92 59.37 72.71 -116.09 640.06
## Attention* 9 182 1.40 0.49 1.00 1.38 0.00 1.00 2.00
## IPS* 10 182 1.39 0.49 1.00 1.36 0.00 1.00 2.00
## IncongruencyEffect 11 182 74.99 115.71 44.92 59.37 72.71 -116.09 640.06
## absBiasScore 12 182 0.74 0.28 0.87 0.78 0.18 0.03 1.00
## InconOutlier 13 182 NaN NA NA NaN NA Inf -Inf
## range skew kurtosis se
## Age 77.00 0.58 0.24 1.08
## IncorRespCount 19.00 3.90 17.10 0.21
## SymRespCount 48.00 0.43 -1.51 1.32
## TxtRespCount 48.00 -0.33 -1.62 1.34
## BiasScore 2.00 -0.38 -1.57 0.06
## Con_RT 1050.64 0.65 0.67 12.89
## InCon_RT 1287.56 0.87 1.00 17.07
## Incon.ConRT 756.15 1.75 4.40 8.58
## Attention* 1.00 0.40 -1.85 0.04
## IPS* 1.00 0.45 -1.81 0.04
## IncongruencyEffect 756.15 1.75 4.40 8.58
## absBiasScore 0.97 -1.11 -0.04 0.02
## InconOutlier -Inf NA NA NA#now that outliers are removed, incon effect mean went from 101.74 to 74.99.
7.2 Distributions of Incongruency Effect - Whole Group & Bias Split (InCon Outliers Removed)
data_outliersremoved %>%
ggplot(aes(x=Incon.ConRT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Incongruency Rt Effects (Outliers Removed)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data_outliersremoved %>%
ggplot(aes(x=InCon_RT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Incongruency RT (Outliers Removed)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
data_outliersremoved %>%
ggplot(aes(x=Con_RT)) +
geom_histogram(fill = "grey", color = "black") +
ggtitle("Distribution of Congruency RT (Outliers Removed)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
biased <- subset(data_outliersremoved, data_outliersremoved$BiasScore > 0.8 | data_outliersremoved$BiasScore < -0.55)
neutral <- subset(data_outliersremoved, data_outliersremoved$BiasScore <= 0.8 & data_outliersremoved$BiasScore >= -0.55)
data_grouped <- rbind(biased, neutral)
data_grouped %>%
ggplot(aes(x = Incon.ConRT, fill = Attention, color = Attention))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Incongruency RT Effects By Bias Group Split")
data_grouped %>%
ggplot(aes(x = InCon_RT, fill = Attention, color = Attention))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Incongruent RT By Bias Group Split")
data_grouped %>%
ggplot(aes(x = Con_RT, fill = Attention, color = Attention))+
geom_density(alpha = 0.3)+
ggtitle("Distribution of Congruent RT By Bias Group Split")
# Incon effect dist: Most people seem to fall between 0 and 250 ms incongruency effect- incongruent trials taking longer!7.3 Reaction times by trial type and attention (InCon Outliers Removed)
data_outliersremoved$Attention <- ifelse(data_outliersremoved$BiasScore > 0.8 | data_outliersremoved$BiasScore < -0.55, "Biased", "Neutral")
data_outliersremoved$IPS <- ifelse(data_outliersremoved$BiasScore > 0, "Verbal", "Visual")
# Reaction times
# Biased attender histograms and descriptive statistics
biased_data <- data_outliersremoved[data_outliersremoved$Attention == "Biased", ]
neutral_data <- data_outliersremoved[data_outliersremoved$Attention == "Neutral", ]
# Combine biased and neutral data
data_outliersremoved$Attention <- ifelse(data_outliersremoved$BiasScore > 0.8 | data_outliersremoved$BiasScore < -0.55, "Biased", "Neutral")
biased_data <- data_outliersremoved[data_outliersremoved$Attention == "Biased", ]
combined_data <- rbind(biased_data, neutral_data)
# Calculate means by attention and trial type
library(dplyr)
library(tidyr)
library(ggplot2)
means <- combined_data %>%
group_by(Attention) %>%
summarise(Con_RT = mean(Con_RT, na.rm = TRUE), InCon_RT = mean(InCon_RT, na.rm = TRUE)) %>%
pivot_longer(cols = c(Con_RT, InCon_RT), names_to = "Trial_Type", values_to = "RT")
# Order factor levels for better plotting
means$Attention <- factor(means$Attention, levels = unique(means$Attention))
combined_long <- combined_data %>%
select(Attention, InCon_RT, Con_RT) %>%
pivot_longer(cols = c(InCon_RT, Con_RT), values_to = "RT", names_to = "Condition")
# Create error bars
se_sum <- combined_long %>%
group_by(Attention, Condition) %>%
summarise(
sd = sd(RT, na.rm = TRUE),
n = n(),
mean = mean(RT, na.rm = TRUE)
) %>%
mutate(se = sd/sqrt(n))## `summarise()` has grouped output by 'Attention'. You can override using the
## `.groups` argument.# Create bar plot
ggplot(se_sum, aes(x = Attention, y = mean, fill = Condition)) +
geom_bar(position = position_dodge(0.8), stat = "identity", color = "black", size = 0.5, width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(0.8), width = 0.25, color = "black") +
labs(title = "Mean Reaction Times by Attention and Trial Type",
x = "Attention",
y = "Mean Reaction Time (ms)") +
theme_minimal()
## Incongruency Effect Box Plot (InCon Outliers Removed)
biased_data<- data_outliersremoved[data_outliersremoved$Attention == "Biased", ]
neutral_data<- data_outliersremoved[data_outliersremoved$Attention == "Neutral", ]
combined_data <- rbind(biased_data, neutral_data)
Incongruency_Effect_Data <- data_outliersremoved$IncongruencyEffect
# Create box plot
ggplot(combined_data, aes(x = Attention, y = Incongruency_Effect_Data, fill = Attention)) +
geom_boxplot() +
labs(
title = "Incongruency Effect Box Plot",
x = "Attention",
y = "Incongruency Effect (ms)"
) +
scale_fill_manual(values = c("red", "lightblue")) + # Color for biased and neutral data
theme_minimal()
## Bias Score and Incongruency Effect Correlation (InCon Outliers
Removed)
data_outliersremoved %>%
ggplot(aes(x = BiasScore, y = Incon.ConRT)) +
geom_point(size = 3, alpha = 0.5, fill = "grey") +
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
cor.test(data_outliersremoved$BiasScore, data_outliersremoved$Incon.ConRT)##
## Pearson's product-moment correlation
##
## data: data_outliersremoved$BiasScore and data_outliersremoved$Incon.ConRT
## t = -5.5861, df = 180, p-value = 8.46e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5017781 -0.2530704
## sample estimates:
## cor
## -0.38437687.4 t-test (InCon Outliers Removed)
t.test(Incon.ConRT~Attention, data=data_outliersremoved)##
## Welch Two Sample t-test
##
## data: Incon.ConRT by Attention
## t = -3.3291, df = 79.807, p-value = 0.00132
## alternative hypothesis: true difference in means between group Biased and group Neutral is not equal to 0
## 95 percent confidence interval:
## -107.18506 -26.98088
## sample estimates:
## mean in group Biased mean in group Neutral
## 55.45634 122.539318 T-test Code - Removing Incon Outliers
biased_data<- data_outliersremoved[data_outliersremoved$Attention == "Biased", ]
neutral_data<- data_outliersremoved[data_outliersremoved$Attention == "Neutral", ]
qqnorm(neutral_data$Incon.ConRT, pch = 1, frame = FALSE, main = "Neutral Group: Incon - Con RT (Outliers Removed)")
qqline(neutral_data$Incon.ConRT, col = "steelblue", lwd = 2)
shapiro.test(neutral_data$Incon.ConRT)##
## Shapiro-Wilk normality test
##
## data: neutral_data$Incon.ConRT
## W = 0.9365, p-value = 0.007351# Then test normality assumption in Biased Group
qqnorm(biased_data$Incon.ConRT, pch = 1, frame = FALSE, main = "Biased Group: Incon - Con RT (Outliers Removed)")
qqline(biased_data$Incon.ConRT, col = "darkred", lwd = 2)
shapiro.test(biased_data$Incon.ConRT) ##
## Shapiro-Wilk normality test
##
## data: biased_data$Incon.ConRT
## W = 0.80037, p-value = 5.813e-12# Check that the variance does not differ between groups
# Perform Levene's Test
print(leveneTest(Incon.ConRT ~ Attention, data = data_outliersremoved)) ## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 5.5793 0.01924 *
## 180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Conduct t-test with equal variance assumption
print(t.test(neutral_data$Incon.ConRT, biased_data$Incon.ConRT, var.equal = FALSE)) ##
## Welch Two Sample t-test
##
## data: neutral_data$Incon.ConRT and biased_data$Incon.ConRT
## t = 3.3291, df = 79.807, p-value = 0.00132
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 26.98088 107.18506
## sample estimates:
## mean of x mean of y
## 122.53931 55.456349 Removing outliers- Congruent RT
Con_minus3SD <- mean(data_outliersremoved$Con_RT) - (3* sd(data_outliersremoved$Con_RT))
Con_plus3SD <- mean(data_outliersremoved$Con_RT) + (3* sd(data_outliersremoved$Con_RT))
data_outliersremoved <- data_outliersremoved %>%
mutate(ConOutlier = Con_RT >= Con_plus3SD)
subset(data_outliersremoved, ConOutlier == TRUE)## Subject Age Sex IncorRespCount SymRespCount TxtRespCount BiasScore
## 100 9449a552 57 2 19 17 12 -0.1724138
## Con_RT InCon_RT Incon.ConRT Attention IPS IncongruencyEffect
## 100 1579.715 1494.562 -85.15278 Neutral Visual -85.15278
## absBiasScore InconOutlier ConOutlier
## 100 0.1724138 FALSE TRUEdata_final <- subset(data_outliersremoved, ConOutlier == FALSE)10 T-test with All Outliers Removed
biased_data <- data_final[data_final$Attention == "Biased", ]
neutral_data <- data_final[data_final$Attention == "Neutral", ]
qqnorm(neutral_data$Incon.ConRT, pch = 1, frame = FALSE, main = "Neutral Group: Incon - Con RT (Outliers Removed)")
qqline(neutral_data$Incon.ConRT, col = "steelblue", lwd = 2)
shapiro.test(neutral_data$Incon.ConRT)##
## Shapiro-Wilk normality test
##
## data: neutral_data$Incon.ConRT
## W = 0.92908, p-value = 0.004134# Then test normality assumption in Biased Group
qqnorm(biased_data$Incon.ConRT, pch = 1, frame = FALSE, main = "Biased Group: Incon - Con RT (Outliers Removed)")
qqline(biased_data$Incon.ConRT, col = "darkred", lwd = 2)
shapiro.test(biased_data$Incon.ConRT) ##
## Shapiro-Wilk normality test
##
## data: biased_data$Incon.ConRT
## W = 0.80037, p-value = 5.813e-12# Check that the variance does not differ between groups
# Perform Levene's Test
print(leveneTest(Incon.ConRT ~ Attention, data = data_final)) ## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 4.9887 0.02675 *
## 179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Conduct t-test with equal variance assumption
print(t.test(neutral_data$Incon.ConRT, biased_data$Incon.ConRT, var.equal = FALSE)) ##
## Welch Two Sample t-test
##
## data: neutral_data$Incon.ConRT and biased_data$Incon.ConRT
## t = 3.5444, df = 78.651, p-value = 0.000666
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 31.15857 110.99552
## sample estimates:
## mean of x mean of y
## 126.53339 55.4563410.1 splitting blocks
11 block data
block_data <- read.csv("CardSort Data.csv")
for_calc = data.frame(Subject = character(), block1_mean = numeric(), block2_mean = numeric(), block3_mean = numeric(), block4_mean = numeric(), block1_bias = numeric(), block2_bias = numeric(), block3_bias = numeric(), block4_bias = numeric())
for (subject in unique(data$Subject)){
subject_cols = block_data[block_data$SubjectNumber == subject, ]
subject_block1 <- subject_cols[subject_cols$"Block" == "CardSort_Block1",]
subject_block2 <- subject_cols[subject_cols$"Block" == "CardSort_Block2",]
subject_block3 <- subject_cols[subject_cols$"Block" == "CardSort_Block3",]
subject_block4 <- subject_cols[subject_cols$"Block" == "CardSort_Block4",]
block1_mean <- mean(subject_block1$RT)
block2_mean <- mean(subject_block2$RT)
block3_mean <- mean(subject_block3$RT)
block4_mean <- mean(subject_block4$RT)
block1_incon_rows <- subset(subject_block1, Status == 2)
block1_word <- max(subject_block1$TxtRespCount)
block1_pic <- max(subject_block1$SymRespCount)
block1_correct <- length(block1_incon_rows) - max(subject_block1$IncorrRespCount)
block1_bias <- (block1_word - block1_pic) / block1_correct
block2_incon_rows <- subset(subject_block2, Status == 2)
block2_word <- max(subject_block2$TxtRespCount)-max(subject_block1$TxtRespCount)
block2_pic <- max(subject_block2$SymRespCount)- max(subject_block1$SymRespCount)
block2_correct <- length(block2_incon_rows) - (max(subject_block2$IncorrRespCount)-max(subject_block1$IncorrRespCount))
block2_bias <- (block2_word - block2_pic) / block2_correct
block3_incon_rows <- subset(subject_block3, Status == 2)
block3_word <- max(subject_block3$TxtRespCount)-max(subject_block2$TxtRespCount)
block3_pic <- max(subject_block3$SymRespCount)-max(subject_block2$SymRespCount)
block3_correct <- length(block3_incon_rows) - (max(subject_block3$IncorrRespCount)-max(subject_block2$IncorrRespCount))
block3_bias <- (block3_word - block3_pic) / block3_correct
block4_incon_rows <- subset(subject_block4, Status == 2)
block4_word <- max(subject_block4$TxtRespCount)-max(subject_block3$TxtRespCount)
block4_pic <- max(subject_block4$SymRespCount)-max(subject_block3$SymRespCount)
block4_correct <- length(block4_incon_rows) - (max(subject_block4$IncorrRespCount)-max(subject_block3$IncorrRespCount))
block4_bias <- (block4_word - block4_pic) / block4_correct
new_row1 <- data.frame(Subject = subject, block1_mean = block1_mean, block2_mean = block2_mean, block3_mean = block3_mean, block4_mean = block4_mean, block1_bias = block1_bias, block2_bias = block2_bias, block3_bias = block3_bias, block4_bias = block4_bias)
for_calc<- rbind(for_calc, new_row1)
}
data <- cbind(data, for_calc)
# mean rts for each block biased v neutral
biased_word <- subset(data, data$BiasScore > 0.8)
biased_picture <- subset(data, data$BiasScore < -0.8)
biased <- subset(data, data$BiasScore > 0.8 | data$BiasScore < -0.8)
neutral <- subset(data, data$BiasScore <= 0.8 & data$BiasScore >= -0.8)
biased_means <- colMeans(biased[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
# Calculate means for neutral group
neutral_means <- colMeans(neutral[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
biased_word_means <- colMeans(biased_word[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
biased_picture_means <- colMeans(biased_picture[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
means_df <- data.frame(
Block = rep(c("Block 1", "Block 2", "Block 3", "Block 4"), 2),
MeanBias = c(biased_means, neutral_means),
Group = rep(c("Biased", "Neutral"), each = 4)
)
# subset of neutral
neutral_means_df <- subset(means_df, Group == "Neutral")
# Plotting neutral blocks from means_df
ggplot(neutral_means_df, aes(x = Block, y = MeanBias, group = Group, color = Group)) +
geom_line() +
geom_point() +
labs(title = "Mean Bias Scores for Neutral Group by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(values = c("Neutral" = "blue")) +
theme_minimal()
# Creating means_df for two biased groups and one neutral group
biased_means_df <- data.frame(
Block = rep(c("Block 1", "Block 2", "Block 3", "Block 4"), 3),
MeanBias = c(biased_word_means, biased_picture_means, neutral_means),
Group = rep(c("Biased Word", "Biased Picture", "Neutral"), each = 4)
)
#subset of word biased (positive)
biased_word_means_df <- subset(biased_means_df, Group == "Biased Word")
biased_picture_means_df <- subset(biased_means_df, Group == "Biased Picture")
ggplot(biased_word_means_df, aes(x = Block, y = MeanBias, group = Group, color = Group)) +
geom_line() +
geom_point() +
labs(title = "Mean Bias Scores for Biased Group (Word) by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(values = c("Biased Word" = "blue")) +
theme_minimal()
#subset of picture biased (negative)
ggplot(biased_picture_means_df, aes(x = Block, y = MeanBias, group = Group, color = Group)) +
geom_line() +
geom_point() +
labs(title = "Mean Bias Scores for Biased Group (Picture) by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(values = c("Biased Picture" = "blue")) +
theme_minimal()
ggplot() +
geom_line(data = biased_word_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Word")) +
geom_point(data = biased_word_means_df, aes(x = Block, y = MeanBias, color = "Biased Word")) +
geom_line(data = biased_picture_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Picture")) +
geom_point(data = biased_picture_means_df, aes(x = Block, y = MeanBias, color = "Biased Picture")) +
labs(title = "Mean Bias Scores for Biased Groups by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(name = "Group", values = c("Biased Word" = "purple", "Biased Picture" = "green")) +
theme_minimal()
biased$block1_bias <- abs(biased$block1_bias)
biased$block2_bias <- abs(biased$block2_bias)
biased$block3_bias <- abs(biased$block3_bias)
biased$block4_bias <- abs(biased$block4_bias)
neutral$block1_bias <- abs(neutral$block1_bias)
neutral$block2_bias <- abs(neutral$block2_bias)
neutral$block3_bias <- abs(neutral$block3_bias)
neutral$block4_bias <- abs(neutral$block4_bias)
biased_means <- colMeans(biased[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
# Calculate means for neutral group
neutral_means <- colMeans(neutral[, c("block1_bias", "block2_bias", "block3_bias", "block4_bias")], na.rm = TRUE)
# Combine the means into a new data frame for plotting
means_df <- data.frame(
Block = rep(c("Block 1", "Block 2", "Block 3", "Block 4"), 2),
MeanBias = c(biased_means, neutral_means),
Group = rep(c("Biased", "Neutral"), each = 4)
)
ggplot(means_df, aes(x = Block, y = MeanBias, color = Group, group = Group)) +
geom_line() + # Add lines
geom_point() + # Add points
labs(title = "Mean Absolute Bias Scores by Bias Group and Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(values = c("Biased" = "red", "Neutral" = "blue")) +
theme_minimal()
# Create the combined plot
combined_plot <- ggplot() +
geom_line(data = neutral_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Neutral")) +
geom_point(data = neutral_means_df, aes(x = Block, y = MeanBias, color = "Neutral")) +
geom_line(data = biased_word_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Word")) +
geom_point(data = biased_word_means_df, aes(x = Block, y = MeanBias, color = "Biased Word")) +
geom_line(data = biased_picture_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Picture")) +
geom_point(data = biased_picture_means_df, aes(x = Block, y = MeanBias, color = "Biased Picture")) +
labs(title = "Mean Bias Scores for All Groups by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(name = "Group", values = c("Neutral" = "blue", "Biased Word" = "purple", "Biased Picture" = "green")) +
theme_minimal()
# Display the combined plot
print(combined_plot)
# trying to make it with error bars
# Step 1: Calculate descriptive statistics and store in a variable
biased_word <- subset(data, data$BiasScore > 0.8)
biased_picture <- subset(data, data$BiasScore < -0.8)
neutral <- subset(data, data$BiasScore <= 0.8 & data$BiasScore >= -0.8)
#biased word cleaning
cleaned_biased_word <- biased_word %>%
select(-c(Subject.1))
descriptives_biased_word <- cleaned_biased_word %>%
select(-c(Subject, Sex, InconOutlier,Attention)) %>%
psych::describe()
# Convert the 'desc_df' to a regular dataframe
bw_descdf <- as.data.frame(descriptives_biased_word)
#biased picture cleaning
cleaned_biased_pic <- biased_picture %>%
select(-c(Subject.1))
descriptives_biased_pic <- cleaned_biased_pic %>%
select(-c(Subject, Sex, InconOutlier,Attention)) %>%
psych::describe()
# Convert the 'desc_df' to a regular dataframe
bp_descdf <- as.data.frame(descriptives_biased_pic)
#cleaning neutral
cleaned_neutral <- neutral %>%
select(-c(block1_mean, block2_mean, block3_mean, block4_mean,
block1_bias, block2_bias, block3_bias, block4_bias, Subject))
descriptives_neutral <- cleaned_neutral %>%
select(-c (Sex, InconOutlier,Attention)) %>%
psych::describe()
# Convert the 'desc_df' to a regular dataframe
n_descdf <- as.data.frame(descriptives_neutral)
# Calculate SE for each block for biased word data
bw_descdf$se <- bw_descdf$sd / sqrt(bw_descdf$n)
# Calculate SE for each block for biased picture data
bp_descdf$se <- bp_descdf$sd / sqrt(bp_descdf$n)
# Calculate SE for each block for neutral data
n_descdf$se <- n_descdf$sd / sqrt(n_descdf$n)
neutral_means_df$se <- NA # Create a new column for SE
# Assuming order of SEs in 'n_descdf' corresponds to the blocks in 'neutral_means_df'
neutral_means_df$se[neutral_means_df$Block == "Block 1"] <- n_descdf$se[5] # BiasScore SE for Block 1
neutral_means_df$se[neutral_means_df$Block == "Block 2"] <- n_descdf$se[5] # BiasScore SE for Block 2
neutral_means_df$se[neutral_means_df$Block == "Block 3"] <- n_descdf$se[5] # BiasScore SE for Block 3
neutral_means_df$se[neutral_means_df$Block == "Block 4"] <- n_descdf$se[5] # BiasScore SE for Block 4
# Adding error bars to the plot
se<- n_descdf$se[5]
combined_plot <- ggplot() +
geom_line(data = neutral_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Neutral")) +
geom_point(data = neutral_means_df, aes(x = Block, y = MeanBias, color = "Neutral")) +
geom_errorbar(data = neutral_means_df, aes(x = Block, ymin = MeanBias - se, ymax = MeanBias + se, color = "Neutral"), width = 0.2) +
geom_line(data = biased_word_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Word")) +
geom_point(data = biased_word_means_df, aes(x = Block, y = MeanBias, color = "Biased Word")) +
geom_errorbar(data = biased_word_means_df, aes(x = Block, ymin = MeanBias - se, ymax = MeanBias + se, color = "Biased Word"), width = 0.2) +
geom_line(data = biased_picture_means_df, aes(x = Block, y = MeanBias, group = Group, color = "Biased Picture")) +
geom_point(data = biased_picture_means_df, aes(x = Block, y = MeanBias, color = "Biased Picture")) +
geom_errorbar(data = biased_picture_means_df, aes(x = Block, ymin = MeanBias - se, ymax = MeanBias + se, color = "Biased Picture"), width = 0.2) +
labs(title = "Mean Bias Scores for All Groups by Card Sort Block", x = "Block", y = "Mean Bias Score") +
scale_color_manual(name = "Group", values = c("Neutral" = "blue", "Biased Word" = "purple", "Biased Picture" = "green")) +
theme_minimal()
# Display the combined plot
print(combined_plot)
# Calculating changes between blocks for both groups
change1_bias = biased_means["block2_bias"] - biased_means["block1_bias"]
change2_bias = biased_means["block4_bias"] - biased_means["block3_bias"]
change1_neutral = neutral_means["block2_bias"] - neutral_means["block1_bias"]
change2_neutral = neutral_means["block4_bias"] - neutral_means["block3_bias"]
changes_bias = c(change1_bias, change2_bias)
changes_neutral = c(change1_neutral, change2_neutral)
t_test_all_changes = t.test(changes_bias, changes_neutral, alternative = "two.sided", var.equal = TRUE)
# Print the results
print(t_test_all_changes)##
## Two Sample t-test
##
## data: changes_bias and changes_neutral
## t = -0.26396, df = 2, p-value = 0.8165
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2736995 0.2420583
## sample estimates:
## mean of x mean of y
## 0.03691966 0.05274028biased$block_diff <- abs(biased$block4_bias) - abs(biased$block1_bias)
neutral$block_diff <- abs(neutral$block4_bias) - abs(neutral$block1_bias)
# Perform t-test
t_test_result <- t.test(biased$block_diff, neutral$block_diff, var.equal = FALSE)
# Display the result
t_test_result##
## Welch Two Sample t-test
##
## data: biased$block_diff and neutral$block_diff
## t = -0.33269, df = 102.15, p-value = 0.7401
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1135304 0.0809159
## sample estimates:
## mean of x mean of y
## 0.09663609 0.11294334biased_ttest_b1 <- abs(biased$block1_bias)
neutral_ttest_b2 <- abs(neutral$block1_bias)
# Perform t-test
t_test_result <- t.test(biased_ttest_b1, neutral_ttest_b2, var.equal = FALSE)
# Display the result
t_test_result##
## Welch Two Sample t-test
##
## data: biased_ttest_b1 and neutral_ttest_b2
## t = 10.214, df = 110.47, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3131712 0.4639433
## sample estimates:
## mean of x mean of y
## 0.8482254 0.4596681#convert data frame to long format
data <- data[, !duplicated(colnames(data))]
data <- data %>%
mutate(Attention = ifelse(BiasScore > 0.8 | BiasScore < -0.8, "biased", "neutral"))
long_block_data <- data %>%
gather(key = "Condition", value = "BiasScore", block1_bias:block4_bias) %>%
mutate(Condition = gsub("_bias", "", Condition)) %>%
mutate(BiasScore = abs(BiasScore)) %>%
select(Attention, Subject, Condition, BiasScore)
#block_aov = anova_test(data=long_block_data, dv=BiasScore, wid=Subject, between=Attention, within = Condition)
#print(block_aov)
data <- data[, !duplicated(colnames(data))]
data <- data %>%
mutate(Attention = ifelse(BiasScore > 0.8 | BiasScore < -0.8, "biased", "neutral"))
long_block_data <- data %>%
gather(key = "Condition", value = "BiasScore", block1_bias:block2_bias) %>%
mutate(Condition = gsub("_bias", "", Condition)) %>%
mutate(BiasScore = abs(BiasScore)) %>%
select(Attention, Subject, Condition, BiasScore)
#block_aov = anova_test(data=long_block_data, dv=BiasScore, wid=Subject, between=Attention, within = Condition)
#print(block_aov)df_unique<-data
data_long <- df_unique %>%
select(Subject, block1_bias, block2_bias, block3_bias, block4_bias) %>%
pivot_longer(cols = starts_with("block"), names_to = "Block", values_to = "BiasScore") %>%
mutate(absBiasScore = abs(BiasScore))
# Calculate mean and standard error for each block
se_sum <- data_long %>%
group_by(Block) %>%
summarise(
mean = mean(absBiasScore, na.rm = TRUE),
sd = sd(absBiasScore, na.rm = TRUE),
n = n()
) %>%
mutate(se = sd/sqrt(n))
# Plotting
ggplot(se_sum, aes(x = Block, y = mean, fill = Block)) +
geom_bar(position = position_dodge(0.8), stat = "identity", color = "black", size = 0.5, width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(0.8), width = 0.25, color = "black") +
labs(title = "Mean Absolute Bias Scores by Block",
x = "Block",
y = "Mean Absolute Bias Score") +
theme_minimal() +
theme(legend.position = "none")
data_long <- df_unique %>%
select(Subject, Attention, block1_bias, block2_bias, block3_bias, block4_bias) %>%
pivot_longer(cols = starts_with("block"), names_to = "Block", values_to = "BiasScore") %>%
mutate(absBiasScore = abs(BiasScore))
# Calculate mean and standard error for each block and attention group
se_sum <- data_long %>%
group_by(Attention, Block) %>%
summarise(
mean = mean(absBiasScore, na.rm = TRUE),
sd = sd(absBiasScore, na.rm = TRUE),
n = n()
) %>%
mutate(se = sd/sqrt(n))## `summarise()` has grouped output by 'Attention'. You can override using the
## `.groups` argument.# Plotting
ggplot(se_sum, aes(x = Block, y = mean, fill = Attention)) +
geom_bar(position = position_dodge(0.8), stat = "identity", color = "black", size = 0.5, width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(0.8), width = 0.25, color = "black") +
scale_fill_manual(values = c("neutral" = "blue", "biased" = "red")) +
labs(title = "Mean Absolute Bias Scores by Block and Attention",
x = "Block",
y = "Mean Absolute Bias Score") +
theme_minimal()
long_df <- data %>%
gather(key = "Block", value = "RT", block1_mean:block4_mean)
# Convert block names to a factor to ensure proper ordering
long_df$Block <- factor(long_df$Block, levels = c("block1_mean", "block2_mean", "block3_mean", "block4_mean"))
biased <- subset(long_df, long_df$BiasScore > 0.8 | long_df$BiasScore < -0.8)
neutral <- subset(long_df, long_df$BiasScore <= 0.8 & long_df$BiasScore >= -0.8)
# Plot using ggplot2 with a subset of subjects
ggplot(biased, aes(x = Block, y = RT, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(
title = "Reaction Times Across Blocks (biased)",
x = "Block",
y = "Milliseconds"
) +
theme_minimal()+
theme(legend.position = "none")
ggplot(neutral, aes(x = Block, y = RT, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(
title = "Reaction Times Across Blocks (neutral)",
x = "Block",
y = "Milliseconds"
) +
theme_minimal()+
theme(legend.position = "none")
long_df2 <- data %>%
gather(key = "Block", value = "BiasScore", block1_bias:block4_bias)
#Bias Score across blocks
df_unique <- data[, !duplicated(as.list(data))]
data_neutral <- df_unique %>%
filter(Attention == 'neutral')
# Reshape the data for ggplot2
data_neutral_long <- data_neutral %>%
select(Subject, block1_bias, block2_bias, block3_bias, block4_bias) %>%
pivot_longer(cols = starts_with("block"), names_to = "Block", values_to = "BiasScore")
# Plotting
ggplot(data_neutral_long, aes(x = Block, y = BiasScore, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(title = "Bias Scores Across Blocks for Participants (Neutral Attention)",
x = "Block",
y = "Bias Score") +
theme_minimal() +
theme(legend.position = "none")
#biased
data_biased <- df_unique %>%
filter(Attention == 'biased')
# Reshape the data for ggplot2
data_biased_long <- data_biased %>%
select(Subject, block1_bias, block2_bias, block3_bias, block4_bias) %>%
pivot_longer(cols = starts_with("block"), names_to = "Block", values_to = "BiasScore")
# Plotting
ggplot(data_biased_long, aes(x = Block, y = BiasScore, group = Subject, color = Subject)) +
geom_line(position=position_jitter(w=0.05,h=0.05)) +
geom_point() +
labs(title = "Bias Scores Across Blocks for Participants (Biased Attention)",
x = "Block",
y = "Bias Score") +
theme_minimal() +
theme(legend.position = "none")
data_neutral_selected <- data_neutral_long %>%
filter(Subject %in% c("e8b26ab1", "89601069", "08f746fa", "6409a8b2")) # Replace 1, 2, 3 with the subjects you want to plot
# Plot the selected participants
ggplot(data_neutral_selected, aes(x = Block, y = BiasScore, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(
title = "Neutral Participant Strategies",
x = "Block",
y = "Bias Score"
) +
theme_minimal() +
theme(legend.position = "none")
print(data_neutral_long)## # A tibble: 304 × 3
## Subject Block BiasScore
## <chr> <chr> <dbl>
## 1 0156ce12 block1_bias 1
## 2 0156ce12 block2_bias 0.167
## 3 0156ce12 block3_bias 0.667
## 4 0156ce12 block4_bias 0.667
## 5 054ba968 block1_bias 0
## 6 054ba968 block2_bias 0.333
## 7 054ba968 block3_bias 0
## 8 054ba968 block4_bias 0.333
## 9 05546b7f block1_bias 0.333
## 10 05546b7f block2_bias 0.333
## # ℹ 294 more rowsdemographics_data <-read.csv("Copy of CardSortingTask_Analysis_09.28.2023.csv")
names(demographics_data)[names(demographics_data) == "SubjectNumber"] <- "Subject"
merged_df <- merge(demographics_data, df_unique, by = "Subject")
cols_toremove <- c("SymRespCount.x", "TxtRespCount.x", "IncorrRespCount")
merged_df <- merged_df %>% select(-one_of(cols_toremove))
merged_df$Block <- as.factor(sub("CardSort_Block", "", merged_df$Block))
# Split the data into congruent and incongruent trials based on some condition criteria
# Note: You need to adjust 'Condition' based on what defines congruent (1) and incongruent (2) trials in your dataset
congruent_data <- merged_df %>%
filter(Condition == 1) %>%
group_by(Subject, Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE))## `summarise()` has grouped output by 'Subject'. You can override using the
## `.groups` argument.incongruent_data <- merged_df %>%
filter(Condition == 2) %>%
group_by(Subject, Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE))## `summarise()` has grouped output by 'Subject'. You can override using the
## `.groups` argument.# Create a plot for Congruent Trials
p1 <- ggplot(congruent_data, aes(x = Block, y = Mean_RT, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(title = "Mean RT for Congruent Trials", x = "Block", y = "Mean RT (ms)") +
theme_minimal()+
theme(legend.position = "none")
# Create a plot for Incongruent Trials
p2 <- ggplot(incongruent_data, aes(x = Block, y = Mean_RT, group = Subject, color = Subject)) +
geom_line() +
geom_point() +
labs(title = "Mean RT for Incongruent Trials", x = "Block", y = "Mean RT (ms)") +
theme_minimal()+
theme(legend.position = "none")
# Print the plots
print(p1)
print(p2)
overall_congruent <- merged_df %>%
filter(Condition == 1) %>%
group_by(Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE))
overall_incongruent <- merged_df %>%
filter(Condition == 2) %>%
group_by(Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE))
# Plotting overall mean RT for Congruent Trials
p1 <- ggplot(overall_congruent, aes(x = Block, y = Mean_RT)) +
geom_line(color = "blue") +
geom_point(color = "blue") +
labs(title = "Overall Mean RT for Congruent Trials", x = "Block", y = "Mean RT (ms)") +
theme_minimal() +
theme(legend.position = "none")
# Plotting overall mean RT for Incongruent Trials
p2 <- ggplot(overall_incongruent, aes(x = Block, y = Mean_RT)) +
geom_line(color = "red") +
geom_point(color = "red") +
labs(title = "Overall Mean RT for Incongruent Trials", x = "Block", y = "Mean RT (ms)") +
theme_minimal() +
theme(legend.position = "none")
# Print the plots
print(p1)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
print(p2)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
merged_df$Block <- as.factor(sub("CardSort_Block", "", merged_df$Block))
# Calculate overall mean RT for Congruent and Incongruent Trials for each block and create a new column 'Type'
congruent_data <- merged_df %>%
filter(Condition == 1) %>%
group_by(Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE)) %>%
mutate(Type = "Congruent")
incongruent_data <- merged_df %>%
filter(Condition == 2) %>%
group_by(Block) %>%
summarise(Mean_RT = mean(RT, na.rm = TRUE)) %>%
mutate(Type = "Incongruent")
# Combine the datasets
combined_data <- rbind(congruent_data, incongruent_data)
# Plotting overall mean RT for both Congruent and Incongruent Trials
combined_plot <- ggplot(combined_data, aes(x = Block, y = Mean_RT, color = Type, group = Type)) +
geom_line() +
geom_point() +
labs(title = "Overall Mean RT for Congruent and Incongruent Trials", x = "Block", y = "Mean RT (ms)") +
scale_color_manual(values = c("orange", "green")) +
theme_minimal() +
theme(legend.title = element_blank()) # Optionally remove legend title
# Print the combined plot
print(combined_plot)
biased$Group <- "Biased"
neutral$Group <- "Neutral"
# Combine the two datasets into one
combined_data <- rbind(biased, neutral)
# Function to calculate standard error of the mean (SEM)
sem <- function(x) {
return(sd(x, na.rm = TRUE) / sqrt(length(x)))
}
# Aggregate the data to calculate mean RT and SEM for each Block within each Group
mean_rt_scores <- combined_data %>%
group_by(Block, Group) %>%
summarise(
Mean_RT = mean(RT, na.rm = TRUE),
SEM = sem(RT)
)## `summarise()` has grouped output by 'Block'. You can override using the
## `.groups` argument.# Plotting overall mean response time with error bars for Neutral and Biased Groups
combined_plot <- ggplot(mean_rt_scores, aes(x = Block, y = Mean_RT, color = Group, group = Group)) +
geom_line() +
geom_point() +
geom_errorbar(aes(ymin = Mean_RT - SEM, ymax = Mean_RT + SEM), width = 0.2) +
labs(title = "Overall Mean Response Times for Neutral and Biased Groups", x = "Block", y = "Mean Response Time (ms)") +
scale_color_manual(values = c("red", "blue")) +
theme_minimal() +
theme(legend.title = element_blank()) # Optionally remove legend title
# Print the combined plot
print(combined_plot)
parc_cardsort <- read.csv("CardSort_Summary(in).csv")
parc_demographics <- read.csv("Demographics_Summary(in).csv")
parc_merged <- merge(parc_demographics, parc_cardsort, by = "Subject")
maia_data <- read.csv("CardSort Data.csv")
lang_data <- read.csv("CardSortLanguage.csv")
lang_data = lang_data[, c("SubjectNumber", "NativeEnglish", "SecondLang", "WhatSecLang")]
lang_data$L1 = NA
for (subject in lang_data$SubjectNumber){
lang_data$L1[lang_data$NativeEnglish == 1] <- "English"
lang_data$L1[lang_data$NativeEnglish == 2] <- lang_data$WhatSecLang[lang_data$NativeEnglish == 2]
}
# Drop columns using base R
lang_data <- lang_data[, setdiff(names(lang_data), c("NativeEnglish", "SecondLang", "WhatSecLang"))]
maia_merged = merge(lang_data, maia_data, by= "SubjectNumber")
parc_merged = parc_merged[, c("Subject", "L1", "SymRespCount", "TxtRespCount","IncorrRespCount")]
maia_merged = maia_merged[, c("SubjectNumber", "L1", "SymRespCount", "TxtRespCount", "IncorrRespCount")]
names(maia_merged)[names(maia_merged) == "SubjectNumber"] <- "Subject"
merged <- rbind(maia_merged, parc_merged)
englishL1 <- merged[merged$L1 == "English", ]
unique_subjects_eng <- unique(englishL1$Subject)
num_sub_eng <- length(unique_subjects_eng)
not_englishL1 <- merged[merged$L1 != "English", ]
unique_subjects_not_eng <- unique(not_englishL1$Subject)
num_eng_not_L1 <- length(unique_subjects_not_eng)